Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

Xiangkun Li,Yifei Li,Guoqing Sun,Zhihan Lv

doi:10.1155/2021/9923748

Abstract

With the development of science and technology, the introduction of virtual reality technology has pushed the development of human-computer interaction technology to a new height. The combination of virtual reality and human-computer interaction technology has been applied more and more in military simulation, medical rehabilitation, game creation, and other fields. Action is the basis of human behavior. Among them, human behavior and action analysis is an important research direction. In human behavior and action, recognition research based on behavior and action has the characteristics of convenience, intuition, strong interaction, rich expression information, and so on. It has become the first choice of many researchers for human behavior analysis. However, human motion and motion pictures are complex objects with many ambiguous factors, which are difficult to express and process. Traditional motion recognition is usually based on two-dimensional color images, while two-dimensional RGB images are vulnerable to background disturbance, light, environment, and other factors that interfere with human target detection. In recent years, more and more researchers have begun to use fuzzy mathematics theory to identify human behaviors. The plantar pressure data under different motion modes were collected through experiments, and the current gait information was analyzed. The key gait events including toe-off and heel touch were identified by dynamic baseline monitoring. For the error monitoring of key gait events, the screen window is used to filter the repeated recognition events in a certain period of time, which greatly improves the recognition accuracy and provides important gait information for motion pattern recognition. The similarity matching is performed on each template, the correct rate of motion feature extraction is 90.2%, and the correct rate of motion pattern recognition is 96.3%, which verifies the feasibility and effectiveness of human motion recognition based on fuzzy theory. It is hoped to provide processing techniques and application examples for artificial intelligence recognition applications.

Highlights

Because of the diversity of human movements, noisy scenes, and the changeable perspective of camera motion, it is more difficult to recognize human movements
Motion recognition can be fundamentally transformed into the problem of fuzzy control pattern recognition [5,6,7,8]. e development of fuzzy control technology can be divided into the following stages: e First Stage
A new upsurge of research has emerged in the field of human motion recognition

Summary

Introduction

Because of the diversity of human movements, noisy scenes, and the changeable perspective of camera motion, it is more difficult to recognize human movements. Fuzzy recognition has become a pattern of human behavior recognition. Gaofeng et al motion images are only the placement of the hands of the experimenter when walking. E data training set of the joint points of the hands does not contain all the human movements. The human joint point data will be Complexity mixed with irrelevant physical structural characteristics information. When the person picks up the phone, the movement of the lower limbs of the body has little effect on the docking call, which causes interference in the motion recognition. Motion recognition can be fundamentally transformed into the problem of fuzzy control pattern recognition [5,6,7,8]. Motion recognition can be fundamentally transformed into the problem of fuzzy control pattern recognition [5,6,7,8]. e development of fuzzy control technology can be divided into the following stages:

Methods

Findings

Discussion

Conclusion